Abstract

5G is part of next-generation internet systems, and Orthogonal Frequency Division Multiple Access (OFDMA) is a vital technology for efficient and effective spectrum use. The resource-allocation strategies based on OFDMA discussed in this paper help address the needs for data rate, latency, and device connectivity. It is possible to enhance throughput and fairness within the system by dynamically allocating the available bandwidth, partitioned across several orthogonal subcarriers, to multiple users. We focus on the central issues of resource assignment in OFDMA-based heterogeneous networks, including user scheduling, power allocation, and interference suppression. In addition, the use of machine learning and optimization technologies to distribute resources in an adaptive, intelligent manner is presented as the direction for the future. This study proposes to develop scalable, energy-efficient wireless networks by examining existing allocation schemes to enhance them. The varied simulation outputs, along with comparative studies, indicate how the different methods differ in terms of performance trade-offs, such as spectral efficiency, service quality (QoS), and energy consumption. These findings justify concluding that resource allocation strategies using OFDMA improve performance and establish OFDMA's future in many applications, including mobile broadband with ultra-reliable low-latency communications.